{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型训练\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import tqdm\n",
    "\n",
    "base_path = 'stock'\n",
    "\n",
    "market_map = {'主板':0, '中小板':1}\n",
    "exchange_map = {'SZSE':0, 'SSE':1}\n",
    "is_hs_map = {'S':0, 'N':1, 'H':2}\n",
    "\n",
    "area_map = {'深圳': 0, '北京': 1, '吉林': 2, '江苏': 3, '辽宁': 4, '广东': 5, '安徽': 6, '四川': 7, '浙江': 8,\n",
    "            '湖南': 9, '河北': 10, '新疆': 11, '山东': 12, '河南': 13, '山西': 14, '江西': 15, '青海': 16, \n",
    "            '湖北': 17, '内蒙': 18, '海南': 19, '重庆': 20, '陕西': 21, '福建': 22, '广西': 23, '天津': 24, \n",
    "            '云南': 25, '贵州': 26, '甘肃': 27, '宁夏': 28, '黑龙江': 29, '上海': 30, '西藏': 31}\n",
    "\n",
    "industry_map = {'银行': 0, '全国地产': 1, '生物制药': 2, '环境保护': 3, '区域地产': 4, '酒店餐饮': 5, '运输设备': 6, \n",
    " '综合类': 7, '建筑工程': 8, '玻璃': 9, '家用电器': 10, '文教休闲': 11, '其他商业': 12, '元器件': 13, \n",
    " 'IT设备': 14, '其他建材': 15, '汽车服务': 16, '火力发电': 17, '医药商业': 18, '汽车配件': 19, '广告包装': 20, \n",
    " '轻工机械': 21, '新型电力': 22, '饲料': 23, '电气设备': 24, '房产服务': 25, '石油加工': 26, '铅锌': 27, '农业综合': 28,\n",
    " '批发业': 29, '通信设备': 30, '旅游景点': 31, '港口': 32, '机场': 33, '石油贸易': 34, '空运': 35, '医疗保健': 36,\n",
    " '商贸代理': 37, '化学制药': 38, '影视音像': 39, '工程机械': 40, '软件服务': 41, '证券': 42, '化纤': 43, '水泥': 44, \n",
    " '专用机械': 45, '供气供热': 46, '农药化肥': 47, '机床制造': 48, '多元金融': 49, '百货': 50, '中成药': 51, '路桥': 52, \n",
    " '造纸': 53, '食品': 54, '黄金': 55, '化工原料': 56, '矿物制品': 57, '水运': 58, '日用化工': 59, '机械基件': 60, \n",
    " '汽车整车': 61, '煤炭开采': 62, '铁路': 63, '染料涂料': 64, '白酒': 65, '林业': 66, '水务': 67, '水力发电': 68, \n",
    " '互联网': 69, '旅游服务': 70, '纺织': 71, '铝': 72, '保险': 73, '园区开发': 74, '小金属': 75, '铜': 76, '普钢': 77, \n",
    " '航空': 78, '特种钢': 79, '种植业': 80, '出版业': 81, '焦炭加工': 82, '啤酒': 83, '公路': 84, '超市连锁': 85, \n",
    " '钢加工': 86, '渔业': 87, '农用机械': 88, '软饮料': 89, '化工机械': 90, '塑料': 91, '红黄酒': 92, '橡胶': 93, '家居用品': 94,\n",
    " '摩托车': 95, '电器仪表': 96, '服饰': 97, '仓储物流': 98, '纺织机械': 99, '电器连锁': 100, '装修装饰': 101, '半导体': 102, \n",
    " '电信运营': 103, '石油开采': 104, '乳制品': 105, '商品城': 106, '公共交通': 107, '船舶': 108, '陶瓷': 109}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2890/2890 [00:25<00:00, 112.01it/s]\n"
     ]
    }
   ],
   "source": [
    "def JudgeST(x):\n",
    "    if 'ST' in x:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "col = ['open', 'high', 'low', 'pre_close',]\n",
    "\n",
    "def GetMA(df, col_name, rolling_day):\n",
    "    df[col_name + '_'+str(rolling_day)] = df[col_name].rolling(rolling_day).mean()\n",
    "    return df\n",
    "\n",
    "company_info = pd.read_csv(os.path.join(base_path, 'company_info.csv'), encoding='ANSI')\n",
    "company_info['is_ST'] = company_info['name'].apply(JudgeST)\n",
    "# 丢弃一些多余的信息\n",
    "company_info.drop(['index', 'symbol', 'fullname'], axis=1, inplace=True)\n",
    "company_info.dropna(inplace=True)\n",
    "company_info['market'] = company_info['market'].map(market_map)\n",
    "company_info['exchange'] = company_info['exchange'].map(exchange_map)\n",
    "company_info['is_hs'] = company_info['is_hs'].map(is_hs_map)\n",
    "\n",
    "\n",
    "# 读取指数信息\n",
    "stock_index_info = pd.DataFrame()\n",
    "index = ['000001.SH', '000016.SH', '000002.SH', '399001.SZ', '399007.SZ', '399008.SZ', '399101.SZ',\n",
    "         '399102.SZ']\n",
    "for ts_code in index:\n",
    "    tmp_df = pd.read_csv(os.path.join(base_path,  'OldData', ts_code + '_NormalData.csv'))\n",
    "    # 特征工程\n",
    "#         tmp_df = FeatureEngineering(tmp_df)\n",
    "    stock_index_info = pd.concat((stock_index_info, tmp_df)) \n",
    "#     transaction_day = len(tmp_df) \n",
    "tmp_list = list(tmp_df['trade_date'].sort_values())\n",
    "date_map = dict(zip(tmp_list, range(len(tmp_list))))\n",
    "\n",
    "# 读取股票交易信息\n",
    "stock_info = pd.DataFrame()\n",
    "remove_stock = []\n",
    "tmp_list = []\n",
    "for ts_code in tqdm.tqdm(company_info['ts_code']):\n",
    "    tmp_df = pd.read_csv(os.path.join(base_path,  'OldData', ts_code + '_NormalData.csv'))\n",
    "    \n",
    "    # 还需要去除一些停牌时间很久的企业，后期加\n",
    "    if len(tmp_df) < 100:  # 去除一些上市不久的企业\n",
    "        remove_stock.append(ts_code)\n",
    "        continue\n",
    "    tmp_df = tmp_df.sort_values('trade_date', ascending=True).reset_index()\n",
    "#         提取均线信息\n",
    "#     for tmp_col in col:\n",
    "#         for rolling_day in [5, 10, 13, 21, 30]:\n",
    "#             tmp_df = GetMA(tmp_df, tmp_col, rolling_day)\n",
    "    # 特征工程\n",
    "#         tmp_df = FeatureEngineering(tmp_df)\n",
    "    tmp_list.append(tmp_df)\n",
    "stock_info = pd.concat(tmp_list)\n",
    "ts_code_map = dict(zip(stock_info['ts_code'].unique(), range(stock_info['ts_code'].nunique())))\n",
    "stock_info = stock_info.reset_index()\n",
    "stock_info['ts_code_id'] = stock_info['ts_code'].map(ts_code_map)\n",
    "stock_info.drop('index', axis=1, inplace=True)\n",
    "stock_info['trade_date_id'] = stock_info['trade_date'].map(date_map)\n",
    "stock_info['ts_date_id'] = (10000 + stock_info['ts_code_id']) * 10000 + stock_info['trade_date_id']\n",
    "stock_info = stock_info.merge(company_info, how='left', on='ts_code')\n",
    "stock_info_copy = stock_info.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_info = stock_info_copy.copy()\n",
    "col = ['close', 'open', 'high', 'low']\n",
    "feature_col = []\n",
    "for tmp_col in col:\n",
    "    stock_info[tmp_col+'_'+'transform'] = (stock_info[tmp_col] - stock_info['pre_close']) / stock_info['pre_close']\n",
    "    feature_col.append(tmp_col+'_'+'transform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    tmp_df = stock_info[['ts_date_id', 'close']]\n",
    "    tmp_df = tmp_df.rename(columns={'close':'close_shift_{}'.format(i+1)})\n",
    "    feature_col.append('close_shift_{}'.format(i+1))\n",
    "    tmp_df['ts_date_id'] = tmp_df['ts_date_id'] + i + 1\n",
    "    stock_info = stock_info.merge(tmp_df, how='left', on='ts_date_id')\n",
    "stock_info.drop('level_0', axis=1, inplace=True)\n",
    "# stock_info.dropna(inplace=True)\n",
    "\n",
    "for i in range(5):\n",
    "    stock_info['close_shift_{}'.format(i+1)] = (stock_info['close'] - stock_info['close_shift_{}'.format(i+1)]) / stock_info['close_shift_{}'.format(i+1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标签制作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make_label\n",
    "# stock_info = stock_info_copy.copy()\n",
    "use_col = []\n",
    "for i in range(5):\n",
    "    tmp_df = stock_info[['ts_date_id', 'high', 'low']]\n",
    "    tmp_df = tmp_df.rename(columns={'high':'high_shift_{}'.format(i+1), 'low':'low_shift_{}'.format(i+1)})\n",
    "    use_col.append('high_shift_{}'.format(i+1))\n",
    "    use_col.append('low_shift_{}'.format(i+1))\n",
    "    tmp_df['ts_date_id'] = tmp_df['ts_date_id'] - i - 1\n",
    "    stock_info = stock_info.merge(tmp_df, how='left', on='ts_date_id')\n",
    "\n",
    "stock_info.dropna(inplace=True)\n",
    "\n",
    "for i in range(5):\n",
    "    stock_info['high_shift_{}'.format(i+1)] = (stock_info['high_shift_{}'.format(i+1)] - stock_info['close']) / stock_info['close']\n",
    "    stock_info['low_shift_{}'.format(i+1)] = (stock_info['low_shift_{}'.format(i+1)] - stock_info['close']) / stock_info['close']\n",
    "\n",
    "tmp_array = stock_info[use_col].values\n",
    "max_increse = np.max(tmp_array, axis=1)\n",
    "min_increse = np.min(tmp_array, axis=1)\n",
    "stock_info['label_max'] = max_increse\n",
    "stock_info['label_min'] = min_increse\n",
    "stock_info['label_final'] = (stock_info['label_max'] > 0.06) & (stock_info['label_min'] > -0.03)\n",
    "stock_info['label_final'] = stock_info['label_final'].apply(lambda x: int(x))\n",
    "stock_info = stock_info.reset_index()\n",
    "stock_info.drop('index', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "trn_col = ['open', 'high', 'low', 'close', 'pre_close', 'change', 'pct_chg', 'vol', 'amount', 'ts_code_id'] + feature_col\n",
    "label = 'label_final'\n",
    "trn_date_min = 20180101\n",
    "trn_date_max = 20190405\n",
    "val_date_min = 20190406\n",
    "val_date_max = 20190410\n",
    "test_date_min = 20190417\n",
    "test_date_max = 20191218\n",
    "\n",
    "trn_data_idx = (stock_info['trade_date'] >= trn_date_min) & (stock_info['trade_date'] <= trn_date_max)\n",
    "val_data_idx = (stock_info['trade_date'] >= val_date_min) & (stock_info['trade_date'] <= val_date_max)\n",
    "test_data_idx = (stock_info['trade_date'] >= test_date_min) & (stock_info['trade_date'] <= test_date_max)\n",
    "\n",
    "trn = stock_info[trn_data_idx][trn_col].values\n",
    "trn_label = stock_info[trn_data_idx][label].values\n",
    "\n",
    "val = stock_info[val_data_idx][trn_col].values\n",
    "val_label = stock_info[val_data_idx][label].values \n",
    "\n",
    "test = stock_info[test_data_idx][trn_col].values\n",
    "test_label = stock_info[test_data_idx][label].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rate of 0: 0.7934, rate of 1: 0.2066\n",
      "trn data:807864, val data:8329, test data:468603\n",
      "number of features:19\n"
     ]
    }
   ],
   "source": [
    "print('rate of 0: %.4f, rate of 1: %.4f' % (np.sum(trn_label==0)/len(trn_label), np.sum(trn_label==1)/len(trn_label)))\n",
    "print('trn data:%d, val data:%d, test data:%d' % (len(trn), len(val), len(test)))\n",
    "print('number of features:%d' % len(trn_col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[300]\ttraining's binary_logloss: 0.492673\tvalid_1's binary_logloss: 0.400848\n",
      "[600]\ttraining's binary_logloss: 0.488755\tvalid_1's binary_logloss: 0.399869\n",
      "[900]\ttraining's binary_logloss: 0.48672\tvalid_1's binary_logloss: 0.399479\n",
      "[1200]\ttraining's binary_logloss: 0.48498\tvalid_1's binary_logloss: 0.399227\n",
      "[1500]\ttraining's binary_logloss: 0.483507\tvalid_1's binary_logloss: 0.39892\n",
      "[1800]\ttraining's binary_logloss: 0.482178\tvalid_1's binary_logloss: 0.398745\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[2000]\ttraining's binary_logloss: 0.481367\tvalid_1's binary_logloss: 0.398642\n"
     ]
    }
   ],
   "source": [
    "# 模型训练及评价\n",
    "import lightgbm as lgb\n",
    "from sklearn import metrics\n",
    "param = {'num_leaves': 31,\n",
    "         #              'n_estimatores': 3000,\n",
    "         'min_data_in_leaf': 20,\n",
    "         'objective': 'binary',\n",
    "#          'max_depth': 5,\n",
    "         'learning_rate': 0.01,\n",
    "         \"min_child_samples\": 20,\n",
    "         \"boosting\": \"gbdt\",\n",
    "#          \"feature_fraction\": 0.45,\n",
    "         \"bagging_freq\": 1,\n",
    "#          \"bagging_fraction\": 0.8,\n",
    "         \"bagging_seed\": 11,\n",
    "         \"verbosity\": -1}\n",
    "trn_data = lgb.Dataset(trn, trn_label)\n",
    "val_data = lgb.Dataset(val, val_label)\n",
    "num_round = 2000\n",
    "clf = lgb.train(param, trn_data, num_round, valid_sets=[trn_data, val_data], verbose_eval=300,\n",
    "                early_stopping_rounds=100)\n",
    "oof_lgb = clf.predict(val, num_iteration=clf.best_iteration)\n",
    "test_lgb = clf.predict(test, num_iteration=clf.best_iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.872133509424901\n",
      "[[7259    5]\n",
      " [1060    5]]\n",
      "sensitivity:0.500\n"
     ]
    }
   ],
   "source": [
    "oof_lgb_final = np.round(oof_lgb)\n",
    "print(metrics.accuracy_score(val_label, oof_lgb_final))\n",
    "print(metrics.confusion_matrix(val_label, oof_lgb_final))\n",
    "tp = np.sum(((oof_lgb_final == 1) & (val_label == 1)))\n",
    "pp = np.sum(oof_lgb_final == 1)\n",
    "print('sensitivity:%.3f'% (tp/(pp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8446424798816909\n",
      "[[395738     64]\n",
      " [ 72737     64]]\n",
      "sensitivity:0.500\n"
     ]
    }
   ],
   "source": [
    "thresh_hold = 0.6\n",
    "oof_test_final = test_lgb >= thresh_hold\n",
    "print(metrics.accuracy_score(test_label, oof_test_final))\n",
    "print(metrics.confusion_matrix(test_label, oof_test_final))\n",
    "tp = np.sum(((oof_test_final == 1) & (test_label == 1)))\n",
    "pp = np.sum(oof_test_final == 1)\n",
    "print('sensitivity:%.3f'% (tp/(pp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_postive_idx = np.argwhere(oof_test_final == 1).reshape(-1)\n",
    "test_all_idx = np.argwhere(test_data_idx).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看选了哪些股票\n",
    "tmp_col = ['ts_code', 'name', 'trade_date', 'open', 'high', 'low', 'close', 'pre_close',\n",
    "       'change', 'pct_chg', 'amount', 'is_ST', 'label_max', 'label_min', 'label_final']\n",
    "# stock_info.iloc[test_all_idx[test_postive_idx]]\n",
    "\n",
    "tmp_df = stock_info[tmp_col].iloc[test_all_idx[test_postive_idx]].reset_index()\n",
    "# idx_tmp = tmp_df['is_ST'] == 0\n",
    "# tmp_df.loc[idx_tmp, 'is_limit_up'] = (((tmp_df['close'][idx_tmp]-tmp_df['pre_close'][idx_tmp]) / tmp_df['pre_close'][idx_tmp]) > 0.095)\n",
    "# idx_tmp = tmp_df['is_ST'] == 1\n",
    "# tmp_df.loc[idx_tmp, 'is_limit_up'] = (((tmp_df['close'][idx_tmp]-tmp_df['pre_close'][idx_tmp]) / tmp_df['pre_close'][idx_tmp]) > 0.047)\n",
    "\n",
    "tmp_df['is_limit_up'] = tmp_df['close'] == tmp_df['high']\n",
    "\n",
    "buy_df = tmp_df[(tmp_df['is_limit_up']==False)].reset_index()\n",
    "buy_df.drop(['index', 'level_0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46 12\n"
     ]
    }
   ],
   "source": [
    "print(len(buy_df), sum(buy_df['label_final']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock_info.reset_index().head()\n",
    "# 读取指数信息\n",
    "index_df = pd.read_csv(os.path.join(base_path,  'OldData', '000001.SH' + '_NormalData.csv'))\n",
    "tmp_idx = (index_df['trade_date'] >= test_date_min) & (index_df['trade_date'] <= test_date_max)\n",
    "index_df = index_df.loc[tmp_idx].reset_index()\n",
    "index_df.drop('index', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_idx = (index_df['trade_date'] == test_date_min)\n",
    "close1 = index_df[tmp_idx]['close'].values[0]\n",
    "tmp_idx = (index_df['trade_date'] == test_date_max)\n",
    "close2 = index_df[tmp_idx]['close'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20190423  买入 三维工程 (002469.SZ) 3900股，股价：5.03,花费：19617.0,手续费：5.89，剩余现金：80377.11\n",
      "20190424  买入 华铁应急 (603300.SH) 1900股，股价：10.18,花费：19342.0,手续费：5.8，剩余现金：61029.31\n",
      "20190425  买入 贤丰控股 (002141.SZ) 4200股，股价：4.71,花费：19782.0,手续费：5.93，剩余现金：41241.38\n",
      "20190425  止损卖出华铁应急 (603300.SH) 1900股，股价：9.7728,收入：18568.32,手续费：24.14，剩余现金：59785.56，最终亏损：-803.62\n",
      "20190425  止损卖出三维工程 (002469.SZ) 3900股，股价：4.8288,收入：18832.32,手续费：24.48，剩余现金：78593.4，最终亏损：-815.05\n",
      "20190426  止损卖出贤丰控股 (002141.SZ) 4200股，股价：4.521599999999999,收入：18990.72,手续费：24.69，剩余现金：97559.43，最终亏损：-821.9\n",
      "20190507  买入 金发拉比 (002762.SZ) 3300股，股价：5.76,花费：19008.0,手续费：5.7，剩余现金：78545.73\n",
      "20190507  买入 盘龙药业 (002864.SZ) 600股，股价：28.7,花费：17220.0,手续费：5.17，剩余现金：61320.56\n",
      "20190507  买入 蒙娜丽莎 (002918.SZ) 800股，股价：23.03,花费：18424.0,手续费：5.53，剩余现金：42891.03\n",
      "20190507  买入 恒铭达 (002947.SZ) 500股，股价：34.48,花费：17240.0,手续费：5.17，剩余现金：25645.86\n",
      "20190507  买入 永泰能源 (600157.SH) 10000股，股价：1.95,花费：19500.0,手续费：5.85，剩余现金：6140.01\n",
      "20190507  买入 怡球资源 (601388.SH) 3000股，股价：2.02,花费：6060.0,手续费：5，剩余现金：75.01\n",
      "20190508  止损卖出怡球资源 (601388.SH) 3000股，股价：1.95,收入：5850.0,手续费：10.85，剩余现金：5914.16，最终亏损：-225.85\n",
      "20190508  止损卖出永泰能源 (600157.SH) 10000股，股价：1.8719999999999999,收入：18720.0,手续费：24.34，剩余现金：24609.83，最终亏损：-810.19\n",
      "20190508  止盈卖出恒铭达 (002947.SZ) 500股，股价：36.204,收入：18102.0,手续费：23.53，剩余现金：42688.29，最终盈利：833.3\n",
      "20190508  止损卖出蒙娜丽莎 (002918.SZ) 800股，股价：22.1088,收入：17687.04,手续费：22.99，剩余现金：60352.34，最终亏损：-765.48\n",
      "20190510  止盈卖出金发拉比 (002762.SZ) 3300股，股价：6.22,收入：20526.0,手续费：26.68，剩余现金：80851.66，最终盈利：1485.61\n",
      "20190513  到期卖出盘龙药业 (002864.SZ) 600股，股价：29.24,收入：17544.0,手续费：22.81，剩余现金：98372.85，最终盈利：296.03\n",
      "20190520  买入 风神股份 (600469.SH) 3200股，股价：6.1,花费：19520.0,手续费：5.86，剩余现金：78846.99\n",
      "20190521  买入 *ST富控 (600634.SH) 9300股，股价：2.11,花费：19623.0,手续费：5.89，剩余现金：59218.11\n",
      "20190521  止损卖出风神股份 (600469.SH) 3200股，股价：5.9,收入：18880.0,手续费：24.54，剩余现金：78073.56，最终亏损：-670.4\n",
      "20190523  买入 国风塑业 (000859.SZ) 3500股，股价：5.45,花费：19075.0,手续费：5.72，剩余现金：58992.84\n",
      "20190523  买入 露笑科技 (002617.SZ) 3600股，股价：5.31,花费：19116.0,手续费：5.73，剩余现金：39871.1\n",
      "20190523  买入 铜峰电子 (600237.SH) 4800股，股价：4.06,花费：19488.0,手续费：5.85，剩余现金：20377.26\n",
      "20190524  买入 仁和药业 (000650.SZ) 2100股，股价：8.97,花费：18837.0,手续费：5.65，剩余现金：1534.61\n",
      "20190524  止损卖出铜峰电子 (600237.SH) 4800股，股价：3.8975999999999993,收入：18708.48,手续费：24.32，剩余现金：20218.77，最终亏损：-809.69\n",
      "20190524  止损卖出露笑科技 (002617.SZ) 3600股，股价：5.097599999999999,收入：18351.36,手续费：23.86，剩余现金：38546.27，最终亏损：-794.23\n",
      "20190524  止损卖出*ST富控 (600634.SH) 9300股，股价：2.0256,收入：18838.08,手续费：24.49，剩余现金：57359.86，最终亏损：-815.3\n",
      "20190527  买入 ST中南 (002445.SZ) 10400股，股价：1.82,花费：18928.0,手续费：5.68，剩余现金：38426.18\n",
      "20190527  买入 *ST利源 (002501.SZ) 13200股，股价：1.44,花费：19008.0,手续费：5.7，剩余现金：19412.48\n",
      "20190527  买入 *ST九有 (600462.SH) 11700股，股价：1.62,花费：18954.0,手续费：5.69，剩余现金：452.79\n",
      "20190527  止盈卖出仁和药业 (000650.SZ) 2100股，股价：9.418500000000002,收入：19778.85,手续费：25.71，剩余现金：20205.93，最终盈利：910.49\n",
      "20190528  止盈卖出国风塑业 (000859.SZ) 3500股，股价：5.7225,收入：20028.75,手续费：26.04，剩余现金：40208.64，最终盈利：921.99\n",
      "20190529  买入 *ST美丽 (000010.SZ) 6300股，股价：3.11,花费：19593.0,手续费：5.88，剩余现金：20609.76\n",
      "20190529  买入 ST冠福 (002102.SZ) 7800股，股价：2.52,花费：19656.0,手续费：5.9，剩余现金：947.87\n",
      "20190530  止盈卖出*ST美丽 (000010.SZ) 6300股，股价：3.2655,收入：20572.65,手续费：26.74，剩余现金：21493.77，最终盈利：947.03\n",
      "20190530  止损卖出*ST九有 (600462.SH) 11700股，股价：1.5552000000000001,收入：18195.84,手续费：23.65，剩余现金：39665.96，最终亏损：-787.5\n",
      "20190531  到期卖出*ST利源 (002501.SZ) 13200股，股价：1.46,收入：19272.0,手续费：25.05，剩余现金：58912.91，最终盈利：233.24\n",
      "20190531  到期卖出ST中南 (002445.SZ) 10400股，股价：1.83,收入：19032.0,手续费：24.74，剩余现金：77920.16，最终盈利：73.58\n",
      "20190603  止损卖出ST冠福 (002102.SZ) 7800股，股价：2.4192,收入：18869.76,手续费：24.53，剩余现金：96765.39，最终亏损：-816.67\n",
      "20190610  买入 海王生物 (000078.SZ) 5700股，股价：3.38,花费：19266.0,手续费：5.78，剩余现金：77493.61\n",
      "20190611  买入 世纪星源 (000005.SZ) 6700股，股价：2.85,花费：19095.0,手续费：5.73，剩余现金：58392.88\n",
      "20190611  买入 *ST秋林 (600891.SH) 12900股，股价：1.49,花费：19221.0,手续费：5.77，剩余现金：39166.12\n",
      "20190612  买入 华映科技 (000536.SZ) 7500股，股价：2.59,花费：19425.0,手续费：5.83，剩余现金：19735.29\n",
      "20190612  买入 新能泰山 (000720.SZ) 3800股，股价：5.05,花费：19190.0,手续费：5.76，剩余现金：539.53\n",
      "20190613  止盈卖出*ST秋林 (600891.SH) 12900股，股价：1.6,收入：20640.0,手续费：26.83，剩余现金：21152.7，最终盈利：1386.4\n",
      "20190614  买入 真视通 (002771.SZ) 1700股，股价：11.27,花费：19159.0,手续费：5.75，剩余现金：1987.95\n",
      "20190614  止盈卖出新能泰山 (000720.SZ) 3800股，股价：5.3025,收入：20149.5,手续费：26.19，剩余现金：22111.26，最终盈利：927.55\n",
      "20190614  止盈卖出华映科技 (000536.SZ) 7500股，股价：2.7195,收入：20396.25,手续费：26.52，剩余现金：42480.99，最终盈利：938.91\n",
      "20190614  到期卖出海王生物 (000078.SZ) 5700股，股价：3.36,收入：19152.0,手续费：24.9，剩余现金：61608.1，最终亏损：-144.68\n",
      "20190617  买入 恒铭达 (002947.SZ) 500股，股价：38.34,花费：19170.0,手续费：5.75，剩余现金：42432.35\n",
      "20190617  止损卖出真视通 (002771.SZ) 1700股，股价：10.819199999999999,收入：18392.64,手续费：23.91，剩余现金：60801.08，最终亏损：-796.02\n",
      "20190617  到期卖出世纪星源 (000005.SZ) 6700股，股价：2.8,收入：18760.0,手续费：24.39，剩余现金：79536.69，最终亏损：-365.12\n",
      "20190619  止盈卖出恒铭达 (002947.SZ) 500股，股价：40.257000000000005,收入：20128.5,手续费：26.17，剩余现金：99639.02，最终盈利：926.58\n",
      "20190621  买入 森马服饰 (002563.SZ) 1800股，股价：10.59,花费：19062.0,手续费：5.72，剩余现金：80571.3\n",
      "20190627  止盈卖出森马服饰 (002563.SZ) 1800股，股价：11.1195,收入：20015.1,手续费：26.02，剩余现金：100560.38，最终盈利：921.36\n",
      "20190722  买入 正虹科技 (000702.SZ) 3300股，股价：6.04,花费：19932.0,手续费：5.98，剩余现金：80622.4\n",
      "20190723  买入 宇环数控 (002903.SZ) 1600股，股价：12.38,花费：19808.0,手续费：5.94，剩余现金：60808.46\n",
      "20190725  买入 *ST东南 (002263.SZ) 10000股，股价：2.03,花费：20300.0,手续费：6.09，剩余现金：40502.37\n",
      "20190725  止盈卖出宇环数控 (002903.SZ) 1600股，股价：12.999,收入：20798.4,手续费：27.04，剩余现金：61273.73，最终盈利：957.42\n",
      "20190726  止盈卖出正虹科技 (000702.SZ) 3300股，股价：6.3420000000000005,收入：20928.6,手续费：27.21，剩余现金：82175.13，最终盈利：963.41\n",
      "20190731  到期卖出*ST东南 (002263.SZ) 10000股，股价：2.01,收入：20100.0,手续费：26.13，剩余现金：102249.0，最终亏损：-232.22\n",
      "20190806  买入 *ST节能 (000820.SZ) 11600股，股价：1.76,花费：20416.0,手续费：6.12，剩余现金：81826.87\n",
      "20190807  止损卖出*ST节能 (000820.SZ) 11600股，股价：1.67,收入：19372.0,手续费：25.18，剩余现金：101173.69，最终亏损：-1075.31\n",
      "20190812  买入 安正时尚 (603839.SH) 1800股，股价：11.21,花费：20178.0,手续费：6.05，剩余现金：80989.63\n",
      "20190813  止盈卖出安正时尚 (603839.SH) 1800股，股价：11.770500000000002,收入：21186.9,手续费：27.54，剩余现金：102148.99，最终盈利：975.3\n",
      "20190830  买入 誉衡药业 (002437.SZ) 5800股，股价：3.47,花费：20126.0,手续费：6.04，剩余现金：82016.95\n",
      "20190904  止盈卖出誉衡药业 (002437.SZ) 5800股，股价：3.6435000000000004,收入：21132.3,手续费：27.47，剩余现金：103121.78，最终盈利：972.79\n",
      "20191010  买入 长源电力 (000966.SZ) 4100股，股价：5.0,花费：20500.0,手续费：6.15，剩余现金：82615.63\n",
      "20191011  止盈卖出长源电力 (000966.SZ) 4100股，股价：5.3,收入：21730.0,手续费：28.25，剩余现金：104317.38，最终盈利：1195.6\n",
      "20191025  买入 美盛文化 (002699.SZ) 3000股，股价：6.92,花费：20760.0,手续费：6.23，剩余现金：83551.15\n",
      "20191028  止盈卖出美盛文化 (002699.SZ) 3000股，股价：7.6,收入：22800.0,手续费：29.64，剩余现金：106321.51，最终盈利：2004.13\n",
      "20191112  买入 贵人鸟 (603555.SH) 6000股，股价：3.52,花费：21120.0,手续费：6.34，剩余现金：85195.18\n",
      "20191114  止损卖出贵人鸟 (603555.SH) 6000股，股价：3.3792,收入：20275.2,手续费：26.36，剩余现金：105444.02，最终亏损：-877.49\n",
      "20191122  买入 隆鑫通用 (603766.SH) 5900股，股价：3.57,花费：21063.0,手续费：6.32，剩余现金：84374.7\n",
      "20191128  到期卖出隆鑫通用 (603766.SH) 5900股，股价：3.58,收入：21122.0,手续费：27.46，剩余现金：105469.24，最终盈利：25.22\n"
     ]
    }
   ],
   "source": [
    "from imp import reload\n",
    "import Account\n",
    "reload(Account)\n",
    "money_init = 100000\n",
    "account = Account.Account(money_init)\n",
    "account.BackTest(buy_df, stock_info_copy, index_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>name</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>buy_date</th>\n",
       "      <th>buy_num</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>sell_date</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>002469.SZ</td>\n",
       "      <td>三维工程</td>\n",
       "      <td>5.03</td>\n",
       "      <td>20190423</td>\n",
       "      <td>3900</td>\n",
       "      <td>4.8288</td>\n",
       "      <td>20190425</td>\n",
       "      <td>-815.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>603300.SH</td>\n",
       "      <td>华铁应急</td>\n",
       "      <td>10.18</td>\n",
       "      <td>20190424</td>\n",
       "      <td>1900</td>\n",
       "      <td>9.7728</td>\n",
       "      <td>20190425</td>\n",
       "      <td>-803.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002141.SZ</td>\n",
       "      <td>贤丰控股</td>\n",
       "      <td>4.71</td>\n",
       "      <td>20190425</td>\n",
       "      <td>4200</td>\n",
       "      <td>4.5216</td>\n",
       "      <td>20190426</td>\n",
       "      <td>-821.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002762.SZ</td>\n",
       "      <td>金发拉比</td>\n",
       "      <td>5.76</td>\n",
       "      <td>20190507</td>\n",
       "      <td>3300</td>\n",
       "      <td>6.22</td>\n",
       "      <td>20190510</td>\n",
       "      <td>1485.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002864.SZ</td>\n",
       "      <td>盘龙药业</td>\n",
       "      <td>28.7</td>\n",
       "      <td>20190507</td>\n",
       "      <td>600</td>\n",
       "      <td>29.24</td>\n",
       "      <td>20190513</td>\n",
       "      <td>296.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>002918.SZ</td>\n",
       "      <td>蒙娜丽莎</td>\n",
       "      <td>23.03</td>\n",
       "      <td>20190507</td>\n",
       "      <td>800</td>\n",
       "      <td>22.1088</td>\n",
       "      <td>20190508</td>\n",
       "      <td>-765.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>002947.SZ</td>\n",
       "      <td>恒铭达</td>\n",
       "      <td>34.48</td>\n",
       "      <td>20190507</td>\n",
       "      <td>500</td>\n",
       "      <td>36.204</td>\n",
       "      <td>20190508</td>\n",
       "      <td>833.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>600157.SH</td>\n",
       "      <td>永泰能源</td>\n",
       "      <td>1.95</td>\n",
       "      <td>20190507</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.872</td>\n",
       "      <td>20190508</td>\n",
       "      <td>-810.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>601388.SH</td>\n",
       "      <td>怡球资源</td>\n",
       "      <td>2.02</td>\n",
       "      <td>20190507</td>\n",
       "      <td>3000</td>\n",
       "      <td>1.95</td>\n",
       "      <td>20190508</td>\n",
       "      <td>-225.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>600469.SH</td>\n",
       "      <td>风神股份</td>\n",
       "      <td>6.1</td>\n",
       "      <td>20190520</td>\n",
       "      <td>3200</td>\n",
       "      <td>5.9</td>\n",
       "      <td>20190521</td>\n",
       "      <td>-670.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>600634.SH</td>\n",
       "      <td>*ST富控</td>\n",
       "      <td>2.11</td>\n",
       "      <td>20190521</td>\n",
       "      <td>9300</td>\n",
       "      <td>2.0256</td>\n",
       "      <td>20190524</td>\n",
       "      <td>-815.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>000859.SZ</td>\n",
       "      <td>国风塑业</td>\n",
       "      <td>5.45</td>\n",
       "      <td>20190523</td>\n",
       "      <td>3500</td>\n",
       "      <td>5.7225</td>\n",
       "      <td>20190528</td>\n",
       "      <td>921.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>002617.SZ</td>\n",
       "      <td>露笑科技</td>\n",
       "      <td>5.31</td>\n",
       "      <td>20190523</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0976</td>\n",
       "      <td>20190524</td>\n",
       "      <td>-794.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>600237.SH</td>\n",
       "      <td>铜峰电子</td>\n",
       "      <td>4.06</td>\n",
       "      <td>20190523</td>\n",
       "      <td>4800</td>\n",
       "      <td>3.8976</td>\n",
       "      <td>20190524</td>\n",
       "      <td>-809.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000650.SZ</td>\n",
       "      <td>仁和药业</td>\n",
       "      <td>8.97</td>\n",
       "      <td>20190524</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.4185</td>\n",
       "      <td>20190527</td>\n",
       "      <td>910.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>002445.SZ</td>\n",
       "      <td>ST中南</td>\n",
       "      <td>1.82</td>\n",
       "      <td>20190527</td>\n",
       "      <td>10400</td>\n",
       "      <td>1.83</td>\n",
       "      <td>20190531</td>\n",
       "      <td>73.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>002501.SZ</td>\n",
       "      <td>*ST利源</td>\n",
       "      <td>1.44</td>\n",
       "      <td>20190527</td>\n",
       "      <td>13200</td>\n",
       "      <td>1.46</td>\n",
       "      <td>20190531</td>\n",
       "      <td>233.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>600462.SH</td>\n",
       "      <td>*ST九有</td>\n",
       "      <td>1.62</td>\n",
       "      <td>20190527</td>\n",
       "      <td>11700</td>\n",
       "      <td>1.5552</td>\n",
       "      <td>20190530</td>\n",
       "      <td>-787.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000010.SZ</td>\n",
       "      <td>*ST美丽</td>\n",
       "      <td>3.11</td>\n",
       "      <td>20190529</td>\n",
       "      <td>6300</td>\n",
       "      <td>3.2655</td>\n",
       "      <td>20190530</td>\n",
       "      <td>947.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>002102.SZ</td>\n",
       "      <td>ST冠福</td>\n",
       "      <td>2.52</td>\n",
       "      <td>20190529</td>\n",
       "      <td>7800</td>\n",
       "      <td>2.4192</td>\n",
       "      <td>20190603</td>\n",
       "      <td>-816.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>000078.SZ</td>\n",
       "      <td>海王生物</td>\n",
       "      <td>3.38</td>\n",
       "      <td>20190610</td>\n",
       "      <td>5700</td>\n",
       "      <td>3.36</td>\n",
       "      <td>20190614</td>\n",
       "      <td>-144.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>世纪星源</td>\n",
       "      <td>2.85</td>\n",
       "      <td>20190611</td>\n",
       "      <td>6700</td>\n",
       "      <td>2.8</td>\n",
       "      <td>20190617</td>\n",
       "      <td>-365.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>600891.SH</td>\n",
       "      <td>*ST秋林</td>\n",
       "      <td>1.49</td>\n",
       "      <td>20190611</td>\n",
       "      <td>12900</td>\n",
       "      <td>1.6</td>\n",
       "      <td>20190613</td>\n",
       "      <td>1386.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>000536.SZ</td>\n",
       "      <td>华映科技</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20190612</td>\n",
       "      <td>7500</td>\n",
       "      <td>2.7195</td>\n",
       "      <td>20190614</td>\n",
       "      <td>938.907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>000720.SZ</td>\n",
       "      <td>新能泰山</td>\n",
       "      <td>5.05</td>\n",
       "      <td>20190612</td>\n",
       "      <td>3800</td>\n",
       "      <td>5.3025</td>\n",
       "      <td>20190614</td>\n",
       "      <td>927.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>002771.SZ</td>\n",
       "      <td>真视通</td>\n",
       "      <td>11.27</td>\n",
       "      <td>20190614</td>\n",
       "      <td>1700</td>\n",
       "      <td>10.8192</td>\n",
       "      <td>20190617</td>\n",
       "      <td>-796.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>002947.SZ</td>\n",
       "      <td>恒铭达</td>\n",
       "      <td>38.34</td>\n",
       "      <td>20190617</td>\n",
       "      <td>500</td>\n",
       "      <td>40.257</td>\n",
       "      <td>20190619</td>\n",
       "      <td>926.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>002563.SZ</td>\n",
       "      <td>森马服饰</td>\n",
       "      <td>10.59</td>\n",
       "      <td>20190621</td>\n",
       "      <td>1800</td>\n",
       "      <td>11.1195</td>\n",
       "      <td>20190627</td>\n",
       "      <td>921.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>000702.SZ</td>\n",
       "      <td>正虹科技</td>\n",
       "      <td>6.04</td>\n",
       "      <td>20190722</td>\n",
       "      <td>3300</td>\n",
       "      <td>6.342</td>\n",
       "      <td>20190726</td>\n",
       "      <td>963.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>002903.SZ</td>\n",
       "      <td>宇环数控</td>\n",
       "      <td>12.38</td>\n",
       "      <td>20190723</td>\n",
       "      <td>1600</td>\n",
       "      <td>12.999</td>\n",
       "      <td>20190725</td>\n",
       "      <td>957.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>002263.SZ</td>\n",
       "      <td>*ST东南</td>\n",
       "      <td>2.03</td>\n",
       "      <td>20190725</td>\n",
       "      <td>10000</td>\n",
       "      <td>2.01</td>\n",
       "      <td>20190731</td>\n",
       "      <td>-232.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>000820.SZ</td>\n",
       "      <td>*ST节能</td>\n",
       "      <td>1.76</td>\n",
       "      <td>20190806</td>\n",
       "      <td>11600</td>\n",
       "      <td>1.67</td>\n",
       "      <td>20190807</td>\n",
       "      <td>-1075.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>603839.SH</td>\n",
       "      <td>安正时尚</td>\n",
       "      <td>11.21</td>\n",
       "      <td>20190812</td>\n",
       "      <td>1800</td>\n",
       "      <td>11.7705</td>\n",
       "      <td>20190813</td>\n",
       "      <td>975.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>002437.SZ</td>\n",
       "      <td>誉衡药业</td>\n",
       "      <td>3.47</td>\n",
       "      <td>20190830</td>\n",
       "      <td>5800</td>\n",
       "      <td>3.6435</td>\n",
       "      <td>20190904</td>\n",
       "      <td>972.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>000966.SZ</td>\n",
       "      <td>长源电力</td>\n",
       "      <td>5</td>\n",
       "      <td>20191010</td>\n",
       "      <td>4100</td>\n",
       "      <td>5.3</td>\n",
       "      <td>20191011</td>\n",
       "      <td>1195.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>002699.SZ</td>\n",
       "      <td>美盛文化</td>\n",
       "      <td>6.92</td>\n",
       "      <td>20191025</td>\n",
       "      <td>3000</td>\n",
       "      <td>7.6</td>\n",
       "      <td>20191028</td>\n",
       "      <td>2004.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>603555.SH</td>\n",
       "      <td>贵人鸟</td>\n",
       "      <td>3.52</td>\n",
       "      <td>20191112</td>\n",
       "      <td>6000</td>\n",
       "      <td>3.3792</td>\n",
       "      <td>20191114</td>\n",
       "      <td>-877.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>603766.SH</td>\n",
       "      <td>隆鑫通用</td>\n",
       "      <td>3.57</td>\n",
       "      <td>20191122</td>\n",
       "      <td>5900</td>\n",
       "      <td>3.58</td>\n",
       "      <td>20191128</td>\n",
       "      <td>25.2225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code   name buy_price  buy_date  buy_num sell_price  sell_date  \\\n",
       "0   002469.SZ   三维工程      5.03  20190423     3900     4.8288   20190425   \n",
       "1   603300.SH   华铁应急     10.18  20190424     1900     9.7728   20190425   \n",
       "2   002141.SZ   贤丰控股      4.71  20190425     4200     4.5216   20190426   \n",
       "3   002762.SZ   金发拉比      5.76  20190507     3300       6.22   20190510   \n",
       "4   002864.SZ   盘龙药业      28.7  20190507      600      29.24   20190513   \n",
       "5   002918.SZ   蒙娜丽莎     23.03  20190507      800    22.1088   20190508   \n",
       "6   002947.SZ    恒铭达     34.48  20190507      500     36.204   20190508   \n",
       "7   600157.SH   永泰能源      1.95  20190507    10000      1.872   20190508   \n",
       "8   601388.SH   怡球资源      2.02  20190507     3000       1.95   20190508   \n",
       "9   600469.SH   风神股份       6.1  20190520     3200        5.9   20190521   \n",
       "10  600634.SH  *ST富控      2.11  20190521     9300     2.0256   20190524   \n",
       "11  000859.SZ   国风塑业      5.45  20190523     3500     5.7225   20190528   \n",
       "12  002617.SZ   露笑科技      5.31  20190523     3600     5.0976   20190524   \n",
       "13  600237.SH   铜峰电子      4.06  20190523     4800     3.8976   20190524   \n",
       "14  000650.SZ   仁和药业      8.97  20190524     2100     9.4185   20190527   \n",
       "15  002445.SZ   ST中南      1.82  20190527    10400       1.83   20190531   \n",
       "16  002501.SZ  *ST利源      1.44  20190527    13200       1.46   20190531   \n",
       "17  600462.SH  *ST九有      1.62  20190527    11700     1.5552   20190530   \n",
       "18  000010.SZ  *ST美丽      3.11  20190529     6300     3.2655   20190530   \n",
       "19  002102.SZ   ST冠福      2.52  20190529     7800     2.4192   20190603   \n",
       "20  000078.SZ   海王生物      3.38  20190610     5700       3.36   20190614   \n",
       "21  000005.SZ   世纪星源      2.85  20190611     6700        2.8   20190617   \n",
       "22  600891.SH  *ST秋林      1.49  20190611    12900        1.6   20190613   \n",
       "23  000536.SZ   华映科技      2.59  20190612     7500     2.7195   20190614   \n",
       "24  000720.SZ   新能泰山      5.05  20190612     3800     5.3025   20190614   \n",
       "25  002771.SZ    真视通     11.27  20190614     1700    10.8192   20190617   \n",
       "26  002947.SZ    恒铭达     38.34  20190617      500     40.257   20190619   \n",
       "27  002563.SZ   森马服饰     10.59  20190621     1800    11.1195   20190627   \n",
       "28  000702.SZ   正虹科技      6.04  20190722     3300      6.342   20190726   \n",
       "29  002903.SZ   宇环数控     12.38  20190723     1600     12.999   20190725   \n",
       "30  002263.SZ  *ST东南      2.03  20190725    10000       2.01   20190731   \n",
       "31  000820.SZ  *ST节能      1.76  20190806    11600       1.67   20190807   \n",
       "32  603839.SH   安正时尚     11.21  20190812     1800    11.7705   20190813   \n",
       "33  002437.SZ   誉衡药业      3.47  20190830     5800     3.6435   20190904   \n",
       "34  000966.SZ   长源电力         5  20191010     4100        5.3   20191011   \n",
       "35  002699.SZ   美盛文化      6.92  20191025     3000        7.6   20191028   \n",
       "36  603555.SH    贵人鸟      3.52  20191112     6000     3.3792   20191114   \n",
       "37  603766.SH   隆鑫通用      3.57  20191122     5900       3.58   20191128   \n",
       "\n",
       "     profit  \n",
       "0  -815.047  \n",
       "1  -803.621  \n",
       "2  -821.903  \n",
       "3   1485.61  \n",
       "4   296.027  \n",
       "5   -765.48  \n",
       "6   833.295  \n",
       "7  -810.186  \n",
       "8   -225.85  \n",
       "9    -670.4  \n",
       "10 -815.296  \n",
       "11   921.99  \n",
       "12 -794.232  \n",
       "13 -809.687  \n",
       "14  910.486  \n",
       "15    73.58  \n",
       "16  233.244  \n",
       "17 -787.501  \n",
       "18  947.028  \n",
       "19 -816.667  \n",
       "20 -144.677  \n",
       "21 -365.117  \n",
       "22   1386.4  \n",
       "23  938.907  \n",
       "24  927.549  \n",
       "25 -796.018  \n",
       "26  926.582  \n",
       "27  921.362  \n",
       "28  963.413  \n",
       "29   957.42  \n",
       "30  -232.22  \n",
       "31 -1075.31  \n",
       "32  975.304  \n",
       "33   972.79  \n",
       "34   1195.6  \n",
       "35  2004.13  \n",
       "36 -877.494  \n",
       "37  25.2225  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "账户盈利情况:0.0547\n",
      "上证指数浮动情况:-0.0754\n",
      "交易胜率:0.5263\n",
      "最大回撤率:0.0545\n"
     ]
    }
   ],
   "source": [
    "account_profit = (account.market_value - money_init) / money_init\n",
    "index_profit = (close2 - close1) / close1\n",
    "win_rate = account.victory / (account.victory + account.defeat)\n",
    "print('账户盈利情况:%.4f' % account_profit)\n",
    "print('上证指数浮动情况:%.4f' % index_profit)\n",
    "print('交易胜率:%.4f' % win_rate)\n",
    "print('最大回撤率:%.4f' % account.max_retracement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import Draw\n",
    "reload(Draw)\n",
    "%matplotlib inline\n",
    "index_value = list(index_df[index_df['trade_date']==test_date_min]['pre_close']) + list(index_df.sort_values('trade_date')['close'])\n",
    "Draw.Draw_Market_Value_Change(0, account.market_value_all, index_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\code\\venv2\\lib\\site-packages\\matplotlib\\pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dvk7bGlrTuCYmjJ+QdhlmZgUpuqUt6TuSugqc99eSYtDDn5BWM9qnt5c2CEyV7n9uZtZfUaEtaQowt8B5dwCOAFqBj/c9IqK4YbXMalGV7n9uZtZfwYfHJY0FOoEXC1zkEGA98O8REcWXZmZmZv0V09L+NrAJuLbA+acDAv5T0npJiyQdXmyBZpVUjz3izax+FRTakg4FvgWcRxLchTgAWAKcDhwIvArcm2+xD/UeF0laImnJypUrC3wLs9Fxj/jR8xcfs+oZMbQljQPmA1dExAuFrjgi/iwi/jAinomIZcCfAJOBGcPMf1NEtEZE6+TJkwt9GzNLmb/4mFVPIS3tvwTeBq4fzRtFxDrgXWDv0azHGoR7Z5uZbaOQjmjnA83AO5IAxgPjJa0GDo2IV4ZaSNJi4GsR8XT+9b759bxUjsKtzrl3tpnZNgoJ7eMHzXd2/vElYJWkCcNcxvUUcKOkb5EEfQfwBPCb0ZVsZmbWmEY8PB4RKyJiWd8DeAvYkP+5HegaZtF24D+AB0jOiT8FzI6IzWWo28zMrOEUPSJaRHRGxMz8z/MionWY+dZFxPkRsXNE7BMRF0aEu4WbmZmVyDcMsbrhS4/MrN45tK1u+NIjM6t3Dm0zM7OMcGibmZllhEPbzMwsIxzaZpYdnZ1pV2CWKoe2mWXHsmVpV2CWKoe2mZlZRji0zczMMsKhbWZmlhEObTMzs4xwaJtZdnR0QC6XdhVmqSnk1pxmZrWhtzd5mDUot7TNzMwywqFtZmaWEQ5tMzOzjPA5bTPLjqYmmDAh7SrMUuOWtplVXXNT84DngrW3Q3d3BSoyywaHtplVXXd7N3NnzKW73QFsVgyHtpmZWUY4tM3MzDLCoW1mZpYRDm0zq3udT3WmXYJZWTi0zayu5TpyzLl3DrkOj1lu2efQNrO61tPbM+DZLMsc2maWHS0taVdgliqHtpllR1tb2hWYpcqhbWapaNmlJe0SzDLHoW1mqWib2pZ2CVaMzs60KzAc2mZmVohly9KuwHBom5mZZYZD28yskWThMHcWakyJ76dtZtZIav0wdy4Ha9cWdaVAriNHT28PzU3NdX/nOIe2mZnVjp7iB8FppAF0fHjczMwsIxzaZmZmGeHQNjOzynCHsrJzaJuZlZFvA9pPrXd6yyCHtpnZUEpsJS5bvaysZTSc5mZoakq7iprl0DYzG4pbiaPX0ZFcwlWM7m5ob69MPXXAoW1m1khKCdJS9faWdAmXDc+hbWZWJrmOHB2LO9IuY/scpJnmwVXMzMqkqoN75HKwfj28+2713rNYTU0wYULxy7W0lL2UelFUS1vSdyR1FTjvVEmPSXpP0t2Sdi2pQjOzOldSj/OeHlizpuy1lFV7e3KOulhFDGHaaAoObUlTgLkFzjsReAB4HjgMWAb8uIT6zMzSUcK53+amZprGFd/z2T3OrVAFhbaksUAn8GKB6z0XGAf8aUT8HrgcmCFpr1KKNDOruhLO/Xa3d9M+vQ57PudyyZeYYvkwd9kV2tL+NrAJuLbA+acBD0fEOoCIeB94CjhyuAUkXSRpiaQlK1euLPBtzMwqpKkpuWa43lTz7/Jh7rIbMbQlHQp8CziPJLgLMRF4edC01cCewy0QETdFRGtEtE6ePLnAtzEzq5BSz8fWulL+Ll87vVUuB1L1LpsbZLuhLWkcMB+4IiJeKGK9HwIbBk1bB5TQjdDMrL51LO4g15FOCFiR+k6ZpHTZ3EiXfP0l8DZwfZHrfQsYvAdOBN4vcj1mZnWvd2MvvRt70y5j+3x+uiaMFNrnA83AO5IAxgPjJa0GDo2IV4ZZ7lHgyr4XShb+LHDrqCs2M7Pq8/npmjDSOe3jgYOAqfnHlcCS/M+rJA13uPteYA9JF+VfXwzsBvzrqCs2M7PSucWcadsN7YhYERHL+h4kh7035H9uB7qGWW4t8FXgWklvANcB34iIt8tYu5nZiJqbmgc8V1rLLi1VeZ+SucWcaUWNiBYRnRExM//zvIho3c68PwM+DVwCHBgRN42mUDOzUnS3dzN3xly626vTE7xtalvRyzSNayr+S4VvYVkWWbv/eUVvGBIRPRFxb0S8VMn3MTPLsvbp7cV/qfBlWFuM5mhK1kaj812+zMws06p9NCVNDm0zs6FUscNWzZ8Ht5rh0DazuldSKFaxw1Yp58Ft9DJx//NBfD9tM6t7DkUbSlXvf14mbmmbmZllhEPbzMysUH13SEvpDnAObTMzs0J1d8PcuandAc6hbWaWVR6SdItSOhs2NzXTNK6EAWpS3O6KiNTefDitra2xZMmStMswM7M6N69rHvNmzku7DCQt3d4oo33c0jYzM8sIh7aZmVlGOLTNzMwywqFtZmaWEQ5tMzOzjHBom5mZZYRD28zMLCMc2mZmZhnh0DYzM8sIh7aZmTWsku61niKHtpmZNays3WvdoW1mZpYRDm0zM7OMcGibmZllhEPbzMwsIxzaZmZmGeHQNjMzywhFRNo1bEPSSmB5mVc7CXirzOvMKm+LrbwtBvL22MrbYiBvj60qsS32iYjJI81Uk6FdCZKWRERr2nXUAm+LrbwtBvL22MrbYiBvj63S3BY+PG5mZpYRDm0zM7OMaKTQvintAmqIt8VW3hYDeXts5W0xkLfHVqlti4Y5p21mZpZ1jdTSNjMzyzSHtpmZ2TAkfUzS0ZI+k3YtUOehLenjku6StEbSUkmHpl1TmiRdISkGPWanXVe1SBoj6ZeS2gZN/2+SXpPULemylMqruqG2h6Q9h9hH7kqxzIqTNE3SE5I+kPSmpMv7/e6/SnpZ0tuS/kZSXX9mwvDbQ9I4SesH7RtL0q63kiT9AfAKcAPwhKSfSxqT/10q+0a974C3A58GjgH+DrhX0oR0S0rV0cClwMf7PR5ItaIqkfRR4J+Azw2a/iXg+8A3gROBiySdXv0Kq2u47UHyf+UJBu4j51W3uuqRtDPwC+BBYG/gYuB7kk6WdCxwK3AtcBTJtvl6WrVWw/a2BzAVWM3AfWNmOpVWXv7/yK1AW0RMA/Yn+Qw9I819o247okk6GPgdcExEPJaf9kvgtoj4aarFpUCSgLeB4yLi2bTrqTZJPwE2AQcC/xgRnfnpvwWWRsQl+ddfBC6KiFlp1VoN29ke1wIbI+LPUyyvaiQdDZzV/+/N7xMLgEOBnSLi1Pz0I4FbI2L/VIqtghG2xxrg2Ij4Ylr1VZOkZuDzEXFjv2lPAjcCs0hp36jnlvY0km+Fj/eb9ijJt6JGdAAwEbhd0gZJz+VbmY3iryLiQmBj34T8F5mpDDza0Cj7yDbbI286cHr+lNLbkm6QtGMK9VVFRDw2KKA+AuwHvEDyGdJ/31gK7CNpt+pWWT0jbI/pwGfz+8UaSbdJmpRWrZUWET2DAvsLwL7AfaS4b9RzaE8Efh8DDyWsBvZMqZ60HULyH+8yklMGPwZ+KunAVKuqkoh4aYjJTcBY4OV+01YDEyRNrEphKRlqe0gaD+wF3EzSAj8DmA1cWd3qUvVN4B3gHpLPkC37RkRsAt6jsT5D+m+P/YG7SQJrBnAQcF16pVWHpPGSlgN3Al+LiNdJcd8YW+k3SNGHwIZB09YBDXlOOyLuJNnp+vxA0hnAOcAV6VSVug/zz/33k3X55wnAu9UtJ10R8QGwR79Jr0maB1wFfDuVoqpI0jSSLyhnRMQHkhr6M2Tw9iAJ6z7LJX0TeEDSjhGxPpUiqyC/L5wAXAj8g6SnSDFf6rml/RaQGzRtIvB+CrXUqtdJOps0pIjYAPQycD/pa2F7P0m8Dnyyr8dsvcof5r0L+B8R8VB+8lCfITvTAPvGMNtjsNeBcWy7jepORCyPiCtIOmn+MSnuG/Uc2o+RnGP4ZL9prcCrKdWTKknfl/T1fq/Hkpy7HeqwcSN5FDiu3+tWkm/Mq9IpJz2SjpH0fwdNPhb4z/zhv7ok6WMkh3+fJDmq0GfAviHpv5B8MK+oaoFVNtT2kNQi6WlJ4/rNeixJSNXl9pB0rKR/GjR5I0krO7V9o25DOyJWAI8A1+SvRz0MOAv4ebqVpea3wJWSTpN0BNAJ7Epy/rKR3Qp8Q9Le+Us8vg38MiI2p1xXGp4Bpkj6nqTDJF0EfIvkcsm6lO+MeCewE3AJ0CRpQr4D1q3AVyQdnr8Gdy6wJCK606u4sobbHsAbJKdTb8hvjy8CfwNcFxGDOzPWi+eAz0m6Rsn4BecAxwM/I8V9o57PaQN8jaSHXw/Jt6A7IuIX6ZaUjoi4U1ILSUiPJQnxE/KdKhrZ/wZOIemkt4GkM8mcVCtKSUT0Khls5waSDkgvk3RcrOcbRRwCnJb/+c1+0+dHRJukvyc5JPoOMAY4tcr1Vduw2wM4k2RfeIRkwJFrScY4qEsRsUrSHwL/C/gG8B8kl8M9DZDWvlG312n3yR/qmQGsiojHR5rfGlO+083uwKKIeC/teqx2SDoA+Azwm4h4K+16rHaksW/UfWibmZnVi7o9p21mZlZvHNpmZmYZ4dA2MzPLCIe2mZlZRji0zczMMsKhbWZmlhH/H++GVZigAvLBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 0\n",
    "for ts_code, buy_date, sell_date in zip(account.info['ts_code'], account.info['buy_date'], account.info['sell_date']):\n",
    "    Draw.Draw_Stock(ts_code, stock_info, buy_date, sell_date, left_offset=15, right_offset=15)\n",
    "    num = num + 1\n",
    "    if num > 20:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
